Neural Network Face Posture Analysis via Region Classification
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Solution Overview
Problem
Current technologies face challenges in efficiently and accurately extracting face posture information from images in real-time, particularly in complex environments, leading to high calculation complexity and time consumption.
Innovation Solution
A face posture analysis method utilizing a neural network to obtain face key points from images, which are then input to extract face posture information, including Pitch, Yaw, and Roll, optimizing the process for real-time applications by reducing calculation complexity and time consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional 3D face models and gradient descent methods are used to extract face posture information, then measurement precision can be improved, but device complexity and calculation time increase significantly
Solution Approach 1:
The patent transforms the complex continuous optimization problem into a discrete classification problem by defining multiple candidate posture regions. This parameter transformation allows the system to achieve high measurement precision through region-based classification rather than continuous gradient descent, significantly reducing calculation complexity while maintaining accuracy in face posture information extraction
Solution Approach 2:
The patent segments the face image into multiple candidate posture regions based on key point locations. By dividing the complex posture estimation task into discrete region classifications, the system reduces computational burden while preserving measurement precision through targeted analysis of each segment
2Measurement precision
If traditional 3D face models and gradient descent methods are used to extract face posture information, then measurement precision can be improved, but productivity decreases due to high time consumption
Solution Approach 1:
The patent changes the computational parameters from continuous gradient descent iterations to discrete region-based classification. This transformation dramatically reduces computation time while maintaining measurement precision, enabling real-time processing and improving productivity in face posture information extraction applications
Solution Approach 2:
The patent performs preliminary segmentation of the face image into candidate posture regions before final posture determination. This preliminary action reduces the search space and computation required for precise measurement, enabling faster real-time processing while maintaining accuracy
3Productivity
If face key points are extracted and input into neural network for posture analysis, then productivity is improved through real-time processing, but measurement precision may be affected by calculation complexity reduction
Solution Approach 1:
The patent introduces a spatial dimension by dividing the face image into multiple candidate posture regions. This dimensional transformation allows the neural network to process information in a region-based manner, achieving both real-time processing speed and measurement precision by combining discrete region classification with key point analysis
Data Source
AI summary
A face posture analysis method, an electronic device, and a computer-readable storage medium are provided. The face posture analysis method includes: obtaining a face key point of a to-be-processed face image; and inputting the face key point of the to-be-processed face image into a neural network, to obtain face posture information of the to-be-processed face image output by the neural network.